More than three months ago I wanted to write about the ethnographic butterfly effect and a key informant’s book. But there were strange things happening around games and social media at the time coupled with tragic events in Ferguson, Missouri. So I wrote about those things. It is more than three months later and there are still strange things happening in social media around games and everything in Ferguson, Missouri (and other parts of the United States) is somehow impossibly more sad.

2014 was the year that the major players in qualitative data analysis (QDA) software released native versions for the Mac. For me, the timing was perfect: my dissertation fieldwork in North Dakota had drawn to a close by summer’s end, and my advisor was encouraging me to roll up my sleeves and start working through my material. I wasn’t sure which software package would serve me best, though, and most of the guidance I could find around the Web declined to make head-to-head comparisons. Then, too, I was mindful of the critiques charging that QDA software of any stripe contributes to the mystification of method and amounts to an overpriced means of avoiding index cards, glue, and scissors. I have nothing against index cards, but with operating system issues off the table and student licenses available for under $100, I decided to see if one of these tools could help me to organize my data and get writing.

After sizing up the available options, I downloaded trial versions of two well-known QDA products: NVIVO and Atlas.ti. I knew I was looking for an attractive and intuitive user interface that would allow me to code data in multiple formats: handwritten field notes, interview transcripts, documents I collected in the field. I had little faith that calculating the frequency and co-occurrence of the codes I assigned would unlock some deep, hidden structure of my material. But, taking a cue from one of the founding texts of software studies, I resolved to approach QDA software as an object that “deserves a reciprocation of the richness of thought that went into it, with the care to pay attention to what it says and what it makes palpable or possible.” How, I wondered, would my choice of software package make some kinds of analytical thinking possible and forestall others? What would my choice commit me to? « Read the rest of this entry »

It’s been nearly four years since The Asthma Files (TAF) really took off (as a collaborative ethnographic project housed on an object-oriented platform). In that time our work has included system design and development, data collection, and lots of project coordination. All of this continues today; we’ve learned that the work of designing and building a digital archive is ongoing. By “we” I mean our “Installation Crew”, a collective of social scientists who have met almost every week for years. We’ve also had scores of students, graduate and undergraduates at a number of institutions, use TAF in their courses, through independent studies, and as a space to think through dissertations. In a highly distributed, long-term, ethnographic project like TAF, we’ve derived a number of modest findings from particular sites and studies; the trick is to make sense of the patterned mosaic emerging over time, which is challenging since the very tools we want to use as a window into our work — data visualization apps leveraging semantic tools, for example — are still being developed.

Given TAF’s structure — thematic filing cabinets where data and projects are organized — we have many small findings, related to specific projects. For example, in our most expansive project “Asthmatic Spaces”, comparisons of data produced by state agencies (health and environmental), have made various layers of knowledge gaps visible, spaces where certain types of data, in certain places, is not available (Frickel, 2009). Knowledge gaps can be produced by an array of factors, both within organizations and because of limited support for cross agency collaboration. Another focus of “Asthmatic Spaces” (which aims to compare the asthma epidemic in a half dozen cities in the U.S. and beyond) is to examine how asthma and air quality data are synced up (or not) and made usable across public, private, and nonprofit organizations.

In another project area, “Asthma Knowledges”, we’ve gained a better understanding of how researchers conceptualize asthma as a complex condition, and how this conceptualization has shifted over the last decade, based on emerging epigenetic research. In “Asthma Care” we’ve learned that many excellent asthma education programs have been developed and studied, yet only a fraction of these programs have been successfully implemented, such as in school settings. Our recent focus has been to figure out what factors are at play when programs are successful.

Below I offer three overarching observations, taken from what our “breakout teams” have learned working on various projects over the last few years:

*In the world of asthma research, data production is uneven in myriad ways. This is the case at multiple levels — seen in public health surveillance and our ability to track asthma nationally, as well as at the state and county level; as seen through big data, generated by epigenetic research; in the scale of air quality monitoring, which is conducted at the level of cities and zip codes rather than at neighborhood or street level. Uneven and fragmented data production is to be expected; as ethnographers, we’re interested in what this unevenness and fragmentation tells us about local infrastructure, environmental policy, and the state of health research. Statistics on asthma prevalence, hospitalizations, and medical visits are easy to come by in New York State and California, for example; experts on these data sets are readily found. In Texas and Tennessee, on the other hand, this kind of information is harder to come by; more work is involved in piecing together data narratives and finding people who can speak to the state of asthma locally. Given that most of what we know about asthma comes from studies conducted in major cities, where large, university-anchored medical systems help organize health infrastructure, we wonder what isn’t being learned about asthma and air quality in smaller cities, rural areas, and the suburbs; what does environmental health (and asthma specifically) look like beyond urban ecologies and communities? We find this particularly interesting given the centrality that place has for asthma as a disease condition and epidemic.

*Asthma research is incredibly diffuse and diverse. Part of the idea for The Asthma Files came from Kim Fortun and Mike Fortun’s work on a previous project where they perceived communication gaps between scientists who might otherwise collaborate (on asthma research). Thus, one of our project goals has been to document and characterize contemporary asthma studies, tracing connections made across research centers and disciplines. In the case of a complex and varied disease like asthma — a condition that looks slightly different from one person to the next and is likely produced by a wide composite of factors — the field of research is exponential, with studies that range from pharmaceutical effects and genetic shifts, to demographic groups, comorbidities, and environmental factors like air pollution, pesticides, and allergens. Admittedly, we’ve been slow to map out different research trajectories and clusters while we work to develop better visualization tools in PECE (see Erik Bigras’s February post on TAF’s platform).

What has been clear in our research, however, is that EPA and/or NIEHS-funded centers undertaking transdisciplinary environmental health research seem to advance collaboration and translation better than smaller scale studies. This suggests that government support is greatly needed in efforts to advance understanding of environmental health problems. Transdisciplinary research centers have the capacity to conduct studies with more participants, over longer periods of time, with more data points. Columbia University’s Center for Children’s Environmental Health provides a great example. Engaging scientists from a range of fields, CCCEH’s birth cohort study has tracked more than 700 mother-child pairs from two New York neighborhoods, collecting data on environmental exposures, child health and development. The Center’s most recent findings suggest that air pollution primes children for a cockroach allergy, which is a determinant of childhood asthma. CCCEH’s work has made substantial contributions to understandings of the complexity of environmental health, as seen in the above findings. Of course, these transdisciplinary centers, which require huge grants, are just one node in the larger field of asthma research. What we know from reviewing this larger field is that 1) most of what we know about asthma is based on studies conducted in major cities, 2) that studies on pharmaceuticals greatly outnumber studies on respiratory therapy; that studies on children outnumber studies on adults; that studies on women outnumber studies on men; and that many of the studies focused on how asthma is shaped by race and ethnicity focus on socioeconomic factors and structural violence; finally, 3) that over the last fifty years, advancements in inhaler technology mechanics and design has been limited in key ways, especially when compared to a broader field of medical devices.

*Given the contextual dimensions of environmental health, responses to asthma are shaped by local factors. What’s been most interesting in our collaborative work is to see what comes from comparing projects, programs, and infrastructure across different sites. What communities and organizations enact what kinds of programs to address the asthma epidemic? What resources and structures are needed to make environmental health work happen? Environmental health research of the scale conducted by CCCEH depends on a number of factors and resources — an available study population, institutional resources, an air monitoring network, and medical infrastructure, not to mention an award winning grassroots organization, WE-ACT for Environmental Justice. Infrastructure can be just as uneven and fragmented as the data collected, and the two are often linked: Despite countless studies that associate air pollution and asthma, less than half of all U.S. counties have monitors to track criteria pollutants. And although asthma education programs have been designed and studied for more than two decades now, implementation is uneven, even in the case of the American Lung Association’s long-standing Open Airways for Schools. This is not to say that asthma information and care isn’t standardized; many improvements have been made to standardize diagnosis and treatment in the last decade. Rather, it’s often the form that care takes that varies from place to place. One example of what has been a successful program is the Asthma and Allergy Foundation of America’s Breathmobile program. Piloted in California more than a decade ago, Breathmobiles serve hundreds of California schools each year and more than 5,000 kids. Not only are eleven Breathmobiles in operation in California, but the program has also been replicated in Phoenix, Baltimore, and Mobile, AL. Part of the program’s success in California can be attributed to the work of the state’s AAFA chapter, and partnerships with health organizations, like the University of Southern California and various medical centers. Importantly, California has historically been a leader in responses to environmental health problem.

As we continue our research, in various fieldsites, grow our archive, and implement new data visualization tools, we hope to expand on these findings and further synthesize from our collective work. And beyond what we’re learning about the asthma epidemic and environmental health in the U.S., we’ve also taken many lessons from our collaborative work, and the platform that organizes us.

Although anthropologists have been working with large-scale data sets for quite some time, the term “big data” is currently being used to refer to large, complex sets of data combined from different sources and media that are difficult to wrangle using standard coding schemes or desktop database software. Last year saw a rise in STS approaches that try to grapple with questions of scale in research, and the trend toward data accumulation seems to be continuing unabated. According to IBM, we generate 2.5 quintillion bytes of data each day. This means that 90% of the data in the world was created during the last 2 years.

Big data are often drawn and aggregated from a very large variety of sources, both personal and public, and include everything from social media participation to surveillance footage to consumer buying patterns. Big data sets exhibit complex relationships and yield information to entities who may mine highly personal information in a variety of unpredictable and even potentially violative ways.

The rise of such data sets yields many questions for anthropologists and other researchers interested both in using such data and investigating the techno-cultural implications and ethics of how such data is collected, disseminated, and used by unknown others for public and private purposes. Researchers have called this phenomenon the “politics of the algorithm,” and have called for ways to collect and share big data sets as well as to discuss the implications of their existence.

I asked David Hakken to respond to this issue by answering questions about the direction that big data and associated research frameworks are headed. David is currently directing a Social Informatics (SI) Group in the School of Informatics and Computing (SoIC) at Indiana University Bloomington. Explicitly oriented to the field of Science, Technology, and Society studies, David and his group are developing a notion of social robustness, which calls for developers and designers to take responsibility for the creation and implications of techno-cultural objects, devices, software, and systems. The CASTAC Blog is interested in providing a forum to exchange ideas on the subject of Big Data, in an era in which it seems impossible to return to data innocence.

Patricia: How do you define “big data”?

David: I would add three, essentially epistemological, points to your discussion above. The first is to make explicit how “Big Data” are intimately associated with computing; indeed, the notion that they are a separate species of data is connected to the idea that they are generated more or less “automatically,” as traces normally a part of mediation by computing. Such data are “big” in the sense that they are generated at a much higher rate than are those large-scale, purpose-collected sets that you refer to initially.

The second point is the existence of a parallel phenomenon, “Data Science,” which is a term used in computing circles to refer to a preferred response to “Big Data.” Just as we have had large data sets before Big Data, so we have had formal procedures for dealing with any data. The new claim is that Big Data has such unique properties that it demands its own new Data Science. Also part of the claim is that new procedures, interestingly often referred to as “data mining.” will be the ones characteristic of Data Science. (What are interesting to me are the rank empiricist implications of “data mining.”) Every computing school of which I know is in the process of figuring out how to deal with/“capitalize” on the Data Science opportunity.

The third point is the frequently-made claim that the two together, Big Data and Data Science, provide unique opportunities to study human behavior. Such claims become more than annoying for me when it is asserted that the Big Data/Data Sciences uniquenesses are such that those pursuing them need not pay any attention to any previous attempt to understand human behavior, that only they and they alone are capable of placing the study of human behavior on truly “scientific” footing, again because of their unique scale.

Patricia: Do you think that anthropologists and other researchers should use big data, for instance, using large-scale, global information mined from Twitter or Facebook? Do you view this as “covert research”?

David: We should have the same basic concern about these as we would any other sources of data: Were they gathered with the informed consent of those whose activities created the traces in the first place? Many of the social media sites, game hosts, etc., include permission to gather data as one of their terms of service, to which users agree when they access the site. This situation makes it hard to argue that collection of such data are “covert.” Of course, when such agreement has not been given, any gathered data in my view should not be used.

In the experience of my colleagues, the research problem is not so much the ethical one to which you refer so much as its opposite—that the commercial holders of the Big Data will not allow independent researchers access to it. This situation has led some colleagues to “creative” approaches to gathering big data that have caused some serious problems for my University’s Institutional Review Board.

In sum, I would say that there are ethical issues here that I don’t feel I understand well enough to take a firm position. I would in any particular case begin with whether it makes any sense to use these data to answer the research questions being asked.

Patricia: Who “owns” big data, and how can its owners be held accountable for its integrity and ethical use?

David: I would say that the working assumption of the researchers with whom I am familiar is either the business whose software gathers the traces or the researcher who is able to get users to use their data gathering tool, rather than the users themselves. I take it as a fair point that such data are different from, say, the personal demographic or credit card data that are arguably owned by the individual with whom they are associated. The dangers of selling or similar commercial use of these latter data are legion and clear; of the former, less clear to me, mostly because I don’t know enough about them.

Patricia: What new insights are yielded by the ability to collect and manipulate multi-terrabyte data sets?

David: This is where I am most skeptical. I can see how data on the moves typically made by players in a massive, multiplayer, online game (MMOG) like World of Warcraft ™ would be of interest to an organization that wants to make money building games, and I can see how an argument could be made that analysis of such data could lead to better games and thus be arguably in the interest of the gamers. When it comes to broader implications, say about typical human behavior in general, however, what can be inferred is much more difficult to say. There remain serious sampling issues however big the data set, since the behaviors whose traces are gathered are in no sense that I can see likely to be randomly representative of the population at large. Equally important is a point made repeatedly by my colleague John Paolillo, that the traces gathered are very difficult to use directly in any meaningful sense; that they have to be substantially “cleaned,” and that the principles of such cleaning are difficult to articulate. Paolillo works on Open Source games, where issues of ownership are less salient that they would be in the proprietary games and other software of more general interest.

Equally important: These behavioral traces are generated by activities executed in response to particular stimulations designed into the software. Such stimuli are most likely not typical of those to which humans respond; this is the essence of a technology. How they can be used to make inferences about human behavior in general is beyond my ken.

Let me illustrate in terms of some of my current research on MMOGs. Via game play ethnography, my co-authors (Shad Gross, Nic True) and I arrived at a tripartite basic typology of game moves: those essentially compelled by the physics engine which rendered the game space/time, those responsive to the specific features designed into the game by its developers, and those likely to be based on some analogy with “real life” imported by the player into the game. As the first two are clearly not “normal,” while the third is, we argue that games could be ranked in terms of the ratio between the third and the first two, such ratio constituting an initial indicator of the extent of familiarity with “real life” that could conceivably be inferred from game behavior. Perhaps more important, the kinds of traces to be gathered from play could be changed to help make measures like this easier to develop.

Patricia: What are the epistemological ramifications of big data? Does its existence change what we mean by “knowledge” about behavior and experience in the social sciences?

David: I have already had a stab at the first question. To be explicit about the second: I don’t think so. There are no fundamental knowledge alterations regarding those computer mediations of common human activity, and we don’t know what kind of knowledge is contained in manipulations of data traces generated in response to abnormal, technology-mediated stimuli.

Patricia: boyd and Crawford (2011) argue that asymmetrical access to data creates a new digital divide. What happens when researchers employed for Facebook or Google obtain access to data that is not available to researchers worldwide?

David: I find their argument technically correct, but, as above, I’m not sure how important its implications are. I am reminded of a to-remain-unnamed NSF program officer who once pointed out to a panel on which I served that NSF was unlikely to be asked to fund the really cutting edge research, as this was likely to be done as a closely guarded, corporate secrete.

Patricia: What new skills will researchers need to collect, parse, and analyze big data?

David: This is interesting. When TAing the PhD data analysis course way back in the 1970s, I argued that to take random strolls through data sets in hopes of stumbling on a statistically significant correlation was bad practice, yet this is in my understanding, the approach in “data mining.” We argue in our game research that ethnography can be used to identify the kinds of questions worth asking and thus give a focus, even foster hypothesis testing, as an alternative to such rampant empiricism. Only when such questions are taken seriously will it be possible to articulate what new skills of data analysis are likely to be needed.

Patricia: How can researchers insure data integrity across such mind-boggling large and diverse sets of information?

David: Difficult question if dealing with proprietary software; as with election software, “trust me” is not enough. This is why I have where possible encouraged study of Open Source Projects, like that of Giacomo Poderi in Trento, Italy. Here, at least, the goals of designers and researchers should be aligned.

Patricia: To some extent, anthropologists and other qualitative researchers have always struggled to have their findings respected among colleagues who work with quantitative samples of large-scale data sets. Qualitative approaches seem especially under fire in an era of Big Data. As we move forward, what is/will be the role and importance of qualitative studies in these areas?

David: As I suggested above, in my experience, much of the Data Science research is epistemologically blind. Ethnography can be used to give it some sight. By and large, however, my Data Science colleagues have not found it necessary to respond positively to my offers of collaboration, nor do I think it likely that either their research communities of funders like the NSF, a big pusher for Data Science, will push them toward collaboration with us any time soon.

Patricia: What does the future hold for dealing with “big data,” and where do we go from here?

David: I think we keep asking our questions and turn to Big Data when we can find reason to think that they can help us answer them. I see no reason to jump on the BD/DS bandwagon any time soon.

On behalf of The CASTAC Blog, please join me in thanking David Hakken for contributing his insights into a challenging new area of social science research!